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https://issues.apache.org/jira/browse/SPARK-9936?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14695307#comment-14695307
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Tzach Zohar commented on SPARK-9936:
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[~viirya] indeed! I've just located the problematic line and indeed in master
it's
[fixed|https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala#L134]..
I guess I'll stick to my workaround until 1.5 is released, thanks.
Should I close this issue? Is it a duplicate of an existing issue that I failed
to find? Not sure I know the procedure here...
> decimal precision lost when loading DataFrame from RDD
> ------------------------------------------------------
>
> Key: SPARK-9936
> URL: https://issues.apache.org/jira/browse/SPARK-9936
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.4.0
> Reporter: Tzach Zohar
>
> It seems that when converting an RDD that contains BigDecimals into a
> DataFrame (using SQLContext.createDataFrame without specifying schema),
> precision info is lost, which means saving as Parquet file will fail (Parquet
> tries to verify precision < 18, so fails if it's unset).
> This seems to be similar to
> [SPARK-7196|https://issues.apache.org/jira/browse/SPARK-7196], which fixed
> the same issue for DataFrames created via JDBC.
> To reproduce:
> {code:none}
> scala> val rdd: RDD[(String, BigDecimal)] = sc.parallelize(Seq(("a",
> BigDecimal.valueOf(0.234))))
> rdd: org.apache.spark.rdd.RDD[(String, BigDecimal)] =
> ParallelCollectionRDD[0] at parallelize at <console>:23
> scala> val df: DataFrame = new SQLContext(rdd.context).createDataFrame(rdd)
> df: org.apache.spark.sql.DataFrame = [_1: string, _2: decimal(10,0)]
> scala> df.write.parquet("/data/parquet-file")
> 15/08/13 10:30:07 ERROR executor.Executor: Exception in task 0.0 in stage 0.0
> (TID 0)
> java.lang.RuntimeException: Unsupported datatype DecimalType()
> {code}
> To verify this is indeed caused by the precision being lost, I've tried
> manually changing the schema to include precision (by traversing the
> StructFields and replacing the DecimalTypes with altered DecimalTypes),
> creating a new DataFrame using this updated schema - and indeed it fixes the
> problem.
> I'm using Spark 1.4.0.
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